APPLICATION OF FUZZY MULTIPLE ATTRIBUTE DECISION MAKING IN MINING OPERATIONS

Abstract

Mineral Resources EngineeringVol. 11, No. 01, pp. 59-72 (2002) Technical PapersNo AccessAPPLICATION OF FUZZY MULTIPLE ATTRIBUTE DECISION MAKING IN MINING OPERATIONSAYHAN KESIMAL and ATAC BASCETINAYHAN KESIMALKaradeniz Technical University, Mining Engineering Department, 61080, Trabzon, Turkey Search for more papers by this author and ATAC BASCETINIstanbul University, Mining Engineering Department Avcilar, 34850, Istanbul, Turkey Search for more papers by this author https://doi.org/10.1142/S0950609802000835Cited by:15 PreviousNext AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail AbstractThis paper presents a fuzzy multiple attribute decision making as an innovative tool for criteria aggregation in mining decision problems. So far, various types of formulations or solution methods have been proposed with mining systems, but most of them exclusively considered linear functions as objective functions. Real world study is decision making under subjective constraints of different importance, after using uncertain data (linguistic variables), where compromises between competing criteria are allowed. It seems however that this technique is still very little known in mining. It is one of the aims of this case study to disseminate this technology in many mining fields.The paper is divided into four sections. The first section provides an overview of the underlying concepts and theories of multiple attribute decision making in a fuzzy environment and the scope of this type of search. The second section introduces few applications of fuzzy set theory to mining industry problems reported in the literature. Some of these applications are briefly reviewed. The third section presents two case studies which illustrate the application of the system for equipment selection in surface mining and method selection in underground mining in a fuzzy environment, and highlight the flexible nature of the approach. Details of alternative systems and their criterion of each operation are given. And finally the fourth section presents the concluding remarks. FiguresReferencesRelatedDetailsCited By 15An integrated multiple attribute decision making methodology for quality evaluation of innovation and entrepreneurship education with interval-valued intuitionistic fuzzy informationYongguang Han, Rong Yan and Chang Gou31 Oct 2022 | Journal of Intelligent & Fuzzy Systems, Vol. 11Review on the Development of Mining Method Selection to Identify New Techniques Using a Cascade-Forward Backpropagation Neural NetworkMohamed E. I. 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